A Low-Cost Microcontroller Based Stimulation System to Study Sensory Processing

Asman, Priscella; Jiang, Tianxiao; Öztürk, Musa; Reyna, Juan A.; Ince, Nuri F.

doi:10.1109/ner.2019.8716944

Cited by 2 publications

(1 citation statement)

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“…[57], an Enclustra SX1 FPGA was used to process the tactile data obtained by a novel Hex-O-Skin, while a PIC32 MCU has been utilized to obtain the pressure data from two PVDF tactile sensors [30]. Another 32-bit microcontroller, Teensy 3.2, was adopted as the main unit of a Vibrotactile Stimulation system [58].…”

Section: Hardware Platformsmentioning

confidence: 99%

Efficient Algorithms for Embedded Tactile Data Processing

Younes¹,

Alameh²,

Ibrahim³

et al. 2022

Electronic Skin

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This chapter presents a survey of the existing algorithms and tasks applied for tactile data processing. The presented algorithms and tasks include machine learning, deep learning, feature extraction, and dimensionality reduction. Moreover, this chapter provides guidelines for selecting appropriate hardware platforms for the algorithm's implementation. The algorithms are compared in terms of computational complexity and hardware implementation requirements. A touch modality classification problem is addressed as a case study: FPGA implementations of two algorithms k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are detailed and analyzed. Both algorithms provided real-time classification consuming 236 mW and 1.14 W, respectively. Such results can be improved with the use of approximate computing techniques that provide a trade-off between performance and hardware resources usage. Speedups up to 2× and 3.2× along with 30% and 41% power reduction are obtained for KNN and SVM implementations, respectively.

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Section: Hardware Platformsmentioning

confidence: 99%